A breakthrough in antibiotic resistance detection has been achieved by researchers at the University of Zurich. Using cutting-edge artificial intelligence, the team led by Professor Adrian Egli has successfully harnessed the power of GPT-4, a state-of-the-art AI model, to analyze and identify antibiotic-resistant bacteria. This pioneering research marks a significant step forward in combating the growing threat of antibiotic resistance. ()

The researchers used AI to interpret a common laboratory test known as the Kirby-Bauer disk diffusion test, which helps doctors to determine which antibiotics can or can’t fight a particular bacterial infection.

EUCAST-GPT-expert: AI’s New Tool in the Fight Against Antibiotic Resistance

Based on GPT-4, the scientists created the “EUCAST-GPT-expert”, which follows strict EUCAST (European Committee on Antimicrobial Susceptibility Testing) guidelines for interpreting antimicrobial resistance mechanisms. By incorporating the latest data and expert rules, the system was tested on hundreds of bacterial isolates, helping to identify resistance to life-saving antibiotics.

“Antibiotic resistance is a growing threat worldwide, and we urgently need faster, more reliable tools to detect it,” says Adrian Egli, who led the study. “Our research is the first step toward using AI in routine diagnostics to help doctors identify resistant bacteria more quickly.”

The AI system performed well in detecting certain types of resistance, but it wasn’t perfect. While it was good at spotting bacteria resistant to certain antibiotics, it sometimes flagged bacteria as resistant when they were not, leading to possible delays in treatment. In comparison, human experts were more accurate in determining resistance, but the AI system could still help standardize and speed up the diagnostic process.

Despite the limitations, the study highlights the transformative potential of AI in healthcare. By offering a standardized approach to the interpretation of complex diagnostic tests, AI could eventually help reduce the variability and subjectivity that exists in manual readings, improving patient outcomes.

Adrian Egli emphasizes that more testing and improvements are needed before this AI tool can be used in hospitals. “Our study is an important first step, but we are far from replacing human expertise. Instead, we see AI as a complementary tool that can support microbiologists in their work,” he says.

With further development, AI-based diagnostics could help laboratories worldwide improve the speed and accuracy of detecting drug-resistant infections, helping to preserve the effectiveness of existing antibiotics.

Reference:

1. GPT-4 based AI agents – the new expert system for detection of antimicrobial resistance mechanisms? – (https://journals.asm.org/doi/10.1128/jcm.00689-24)

Source-Eurekalert